2025/12/12 by Scott, John Mahlon, Huang, Hsin-Hsiung
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Maritime Navigation and Safety #Target Tracking and Data Fusion in Sensor Networks
paper · doi:10.48550/arxiv.2512.11707
openalex publication_date 2025/12/12 · openalex created_date 2025/12/16 · openalex updated_date 2026/07/28
The Automatic Identification System (AIS) provides time stamped vessel positions and kinematic reports that enable maritime authorities to monitor traffic. We consider the problem of relabeling AIS trajectories when vessel identifiers are missing, focusing on a challenging nationwide setting in which tracks are heavily downsampled and span diverse operating environments across continental U.S. waters. We propose a hybrid pipeline that first applies a physics-based screening step to project active track endpoints forward in time and select a small set of plausible ancestors for each new observation. A supervised neural classifier then chooses among these candidates, or initiates a new track, using engineered space time and kinematic consistency features. On held out data, this approach improves posit accuracy relative to unsupervised baselines, demonstrating that combining simple motion models with learned disambiguation can scale vessel relabeling to heterogeneous, high volume AIS streams.